Event Relevance Screening Module (ERSM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: TREGA™ — Tax-Relevant Event Governance Architecture™
Parent Standard: Tax-Relevant Event Recognition Standard (TRERS)
Module: ERSM — Event Relevance Screening Module
Operational Layer: Event Relevance Screening Governance Layer
Category: Governance & Enforcement
Subcategory: Tax-Relevant Event Governance
Type: Core Module
Governed Space: Event Relevance Screening
Origin ID: TREGA-TRERS-ERSM-002
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 29 August 2026
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodology Intellectual Property Protection
Canonical Language: English
Canonical Definition
Event Relevance Screening Module (ERSM) is the Core Module withinTax-Relevant Event Recognition Standard (TRERS) that governs
whether a sufficiently distinguishable occurrence possesses
enough plausible economic, operational, transactional, digital,
autonomous, machine-generated, value-related, or other tax-event
relevance to justify continued progression through the TREGA™
recognition process.
ERSM receives a Recognized Occurrence from ORIM and determines
whether that occurrence should remain within the candidate
population for potential Tax-Relevant Event Formation.
Its task is specific:
to screen recognized occurrences for legitimate potential
relevance to tax-event governance without determining legal
taxability, tax jurisdiction, tax liability, or the final
structure of the Tax-Relevant Event.
Operational Role
ORIM answers:Did a sufficiently distinguishable occurrence occur?
ERSM asks the next question:
Does that occurrence have enough potential relevance to justify
further tax-relevant event governance?
This distinction prevents TREGA™ from processing every recognized
occurrence through the complete event lifecycle.
Many genuine occurrences may have no material relationship to
taxation analysis.
For example: a machine performs routine internal diagnostics; a
software service updates a configuration; a warehouse sensor
records temperature; an employee opens an internal application;
an autonomous system performs non-economic optimization; a
platform generates technical housekeeping operations.
These may be real occurrences.
They do not automatically require Tax-Relevant Event Formation.
ERSM acts as the relevance gateway.
Internal Flow
Recognized Occurrence↓
Potential Relevance Signal Identification
↓
Relevance Domain Screening
↓
Material Relevance Assessment
↓
Relevance Sufficiency Determination
↓
Relevant / Non-Relevant /
Unresolved Screening Result
The principal output is:
Event Relevance Screening Result
Where relevance is sufficiently established, the occurrenceproceeds toward governed Recognition Rules.
Operational Space
ERSM governs the space between:a recognized occurrence
and
an occurrence sufficiently relevant to remain within the TRERS
recognition process.
Its operational space includes: relevance-signal identification;
economic relevance; transactional relevance; operational
relevance; digital economic relevance; autonomous-system
relevance; value-related relevance; transfer relevance; exchange
relevance; service relevance; resource relevance; distributed
activity relevance; machine-generated economic relevance;
relevance materiality; relevance uncertainty; non-relevance
determination.
ERSM governs potential relevance to event governance.
It does not govern legal relevance under any particular tax code.
Operational Scope
ERSM may identify plausible relevance where an occurrence ismaterially associated with activity such as: provision of goods;
provision of services; payments; transfers; exchanges;
production; consumption; resource allocation; asset movement;
value creation; value change; value transfer; value
extinguishment; ownership-related activity; commercial execution;
financial activity; digital-service activity; platform-mediated
activity; autonomous purchasing; machine-to-machine exchange;
distributed economic activity; other activity capable of
requiring downstream taxation evaluation.
This list is not itself a legal tax rule.
It defines a broad governance screening space.
Potential Relevance Principle
ERSM deliberately operates on potential relevance, not legaltaxability.
This is essential.
A transaction may later be exempt.
A transfer may later fall outside a particular tax.
A service may not create nexus.
A value movement may be legally neutral.
A machine-generated action may have no independent tax
consequence.
Those questions belong downstream.
ERSM asks only:
Is there a sufficient governance basis to keep this occurrence
within the tax-event analysis lifecycle?
Therefore:
Potential Tax Relevance ≠ Legal Taxability.
Relevance Signal
A relevance signal is any governed characteristic indicating thatthe occurrence may materially matter to downstream taxation
analysis.
Possible signals include: exchange of value; transfer of an asset
or resource; provision or receipt of a service; economic benefit;
economic sacrifice; commercial commitment; payment; settlement;
ownership-related change; operational production; consumption;
resource allocation; cross-border economic activity;
platform-mediated exchange; autonomous commercial action;
machine-to-machine economic execution.
A single relevance signal may be sufficient in some contexts.
In others, several signals may need to be considered together.
Relevance Signal ≠ Taxable Trigger
ERSM must preserve a strict distinction:
Relevance Signal ≠ Taxable Trigger.
A payment may indicate relevance.
It does not prove tax liability.
A transfer may indicate relevance.
It does not prove a taxable disposition.
A service activation may indicate relevance.
It does not establish a jurisdictional tax point.
Recognition infrastructure must not smuggle legal tax conclusions
into the relevance-screening stage.
Economic Relevance
ERSM may identify economic relevance where the occurrencematerially affects, represents, enables, transfers, consumes,
generates, allocates, or relates to economic value or resources.
However:
Economic Relevance ≠ Taxability.
Economic meaning is a reason for further governance, not a legal
conclusion.
Operational Relevance
An occurrence may be operationally significant without appearingimmediately as a conventional transaction.
For example: production completion; delivery; automated resource
consumption; asset deployment; machine execution; platform
fulfilment.
Such activity may require downstream event governance even where
no payment occurs at the same moment.
Therefore:
Payment ≠ Required Condition for Relevance.
Transactional Relevance
A transaction record may provide a strong relevance signal.But ERSM must not assume that:
Transaction Record = Tax-Relevant Event.
A transaction record may represent: initiation; authorization;
partial execution; reversal; synchronization; settlement;
internal technical posting.
Its actual event structure belongs to later governance.
Value-Related Relevance
Where governed value information is available, ERSM may consumeit as a relevance signal.
For example, a governed representation may indicate: Value
Emergence; Value Change; Value Development; Value Degradation;
Value Nullification; Value Restoration; Value Extinction.
But TREGA™ does not recreate Value Governance.
Therefore:
VFM® governs what happened to value. TREGA™ governs whether the
governed value result is relevant to event construction.
Cross-Border Relevance
An occurrence spanning multiple locations, organizations,platforms, or infrastructures may justify continued governance
because different taxation systems may later require evaluation.
However:
Cross-Border Relevance ≠ Tax Jurisdiction.
ERSM does not decide which jurisdiction has taxation rights.
It preserves the occurrence for later jurisdiction-specific
evaluation where appropriate.
Machine and Autonomous Relevance
Autonomous systems create a major relevance-screening challenge.An AI agent may: reserve capacity; compare prices; negotiate;
acquire digital resources; release reservations; initiate
payment; terminate services; transfer machine resources.
Some actions may have economic significance.
Others may be purely technical.
ERSM distinguishes plausible tax-event relevance from ordinary
machine activity.
Therefore:
Autonomous Activity ≠ Economic Relevance by Default.
and:
Economic Relevance ≠ Legal Taxability.
Relevance Materiality
ERSM should prevent insignificant technical or operationalactivity from advancing unnecessarily through the architecture.
Material relevance may depend upon whether an occurrence could
reasonably affect: economic interpretation; transaction
interpretation; value association; participant relationships;
resource transfer; service provision; downstream Event Formation;
later jurisdictional taxation evaluation.
Materiality at this stage remains governance materiality.
It is not a statutory threshold.
Therefore:
Governance Materiality ≠ Legal Tax Materiality.
Relevance Sufficiency
An occurrence becomes relevance-sufficient when enough governedinformation exists to justify continued recognition processing.
This does not require every downstream fact to be known.
ERSM may legitimately advance an occurrence while uncertainty
remains regarding: Event Boundary; Event Timing; Event Location;
participants; value; jurisdiction; tax treatment.
Those questions belong later in TREGA™.
Non-Relevance
ERSM must permit a recognized occurrence to exit the recognitionpathway where no sufficient potential relevance exists.
Examples may include: purely technical system housekeeping;
duplicated internal processing; non-economic diagnostic activity;
irrelevant sensor activity; routine state synchronization;
machine operations with no material relationship to economic
activity.
A non-relevant result means:
the recognized occurrence does not currently justify further
TREGA™ recognition processing.
It does not mean:
the occurrence has been legally determined non-taxable.
Relevance Uncertainty
Some occurrences cannot be confidently screened.For example: an autonomous agent action may have unclear economic
purpose; a platform transfer may lack sufficient business
context; a machine resource movement may or may not be internal;
a digital-asset movement may have ambiguous operational meaning.
ERSM must preserve an unresolved condition rather than forcing a
binary result.
Possible outcomes may include:
Relevant
Not Relevant
Relevance Unresolved
Further Context Required
This allows uncertainty to remain visible and governable.False Positive and False Negative Risk
Relevance screening must control two opposing risks.False Positive
An occurrence proceeds through expensive governance even thoughit has no plausible tax-event relevance.
False Negative
A potentially important occurrence is filtered out prematurely.Both matter.
In machine-scale economies, false positives can create massive
processing volumes.
In fragmented or novel economic environments, false negatives can
remove activity that should have proceeded to Event Formation.
ERSM therefore seeks proportionate relevance screening, not
maximum exclusion.
Governance Requirements
ERSM requires that: screening begins from a RecognizedOccurrence; relevance remains distinct from occurrence existence;
economic, operational, transactional, digital, autonomous, and
value-related signals may be evaluated; relevance criteria remain
jurisdiction-neutral at the architecture layer; potential
relevance remain distinct from legal taxability; legal tax rules
do not become mandatory upstream recognition rules; non-relevance
can be governed explicitly; uncertainty can remain unresolved;
machine-generated activity is not assumed economically relevant;
lack of payment does not automatically eliminate relevance;
cross-border characteristics do not determine Tax Jurisdiction;
relevance decisions remain traceable where materially required.
Governance Logic
ERSM applies four principal questions:What characteristics of this occurrence may make it relevant to
downstream tax-event governance?
Are those characteristics materially meaningful rather than
purely technical?
Is the relevance basis sufficient to justify continued TREGA™
processing?
If relevance cannot yet be established, should the occurrence be
preserved as unresolved rather than rejected?
The transformation is:
Recognized Occurrence
→ Potential Relevance Signals→ Material Relevance Assessment
→ Event Relevance Screening Result
Minimum Implementation Framework
A minimum ERSM implementation must be capable of:1. receiving a Recognized Occurrence from ORIM; 2. identifying
potential relevance signals; 3. evaluating one or more relevant
governance domains; 4. distinguishing technical significance from
potential economic or tax-event relevance; 5. assessing
governance materiality; 6. determining relevance sufficiency; 7.
representing non-relevance; 8. preserving unresolved relevance;
9. recording the screening basis where materially required; 10.
transferring relevance-positive occurrences to ERRM.
ERSM does not prescribe jurisdiction-specific tax logic,
statutory classifications, legal thresholds, AI models, rules
engines, ERP platforms, or tax software.
Governance Outputs
ERSM may produce:Event Relevance Screening Result — the governed outcome of
relevance screening.
Relevant Occurrence — an occurrence sufficiently relevant to
continue through TRERS.
Non-Relevant Occurrence Result — a recognized occurrence that
does not currently justify further event-recognition governance.
Relevance Signal Set — the materially relevant characteristics
supporting screening.
Relevance Domain References — economic, operational,
transactional, digital, autonomous, value-related, or other
relevant domains.
Relevance Materiality Result — the governed assessment of whether
identified relevance is sufficient.
Relevance Uncertainty Indicator — unresolved conditions affecting
the screening outcome.
Further Context Requirement — identification of material
contextual information required before screening can be
completed.
Critical Distinctions
Occurrence Recognition ≠ Event Relevance Screening.ORIM establishes that something sufficiently distinguishable
occurred.
ERSM determines whether that occurrence plausibly matters to
downstream tax-event governance.
Occurrence ≠ Relevant Occurrence.
Not every genuine occurrence requires TREGA™ progression.
Potential Relevance ≠ Legal Taxability.
Relevance keeps an occurrence in governance. It does not create
tax liability.
Relevance Signal ≠ Taxable Trigger.
Operational or economic characteristics cannot replace legal tax
analysis.
Economic Relevance ≠ Tax Liability.
Economic significance does not establish a legal obligation.
Payment ≠ Required Condition for Relevance.
Economically meaningful activity may exist before or without
payment.
Transaction Record ≠ Tax-Relevant Event.
A record remains a representation, not a final event
determination.
Governance Materiality ≠ Statutory Materiality.
ERSM does not establish legal thresholds.
Cross-Border Relevance ≠ Tax Jurisdiction.
Several jurisdictions may later evaluate the same occurrence.
Non-Relevant ≠ Legally Non-Taxable.
ERSM's negative result is methodological, not legal.
Enterprise Application
ERSM provides an important scaling mechanism for enterprises andtax infrastructures processing very high volumes of operational
data.
Without relevance screening, every recognized occurrence could
proceed through:
Formation
↓
Boundary
↓
Timing
↓
Location
↓
Participation
↓
Value
Association
↓
Relationships
↓
Determination
even where most occurrences have no plausible need for taxation
analysis.
In a machine-intensive environment this becomes operationally
unrealistic.
ERSM creates a governed filtering layer between occurrence
recognition and full event construction.
This allows high-volume platforms, banks, industrial systems,
autonomous systems, and digital infrastructures to focus deeper
TREGA™ governance on occurrences with a legitimate relevance
basis while preserving unresolved cases that should not yet be
discarded. Use CasesUse Case 1 — Global Cloud Platform Generating
Billions of Operational Occurrences
A global cloud provider operates millions of servers and
automated services across many regions.
Its systems generate enormous volumes of recognizable
occurrences:
resource allocation,
container creation,
internal network routing,
temporary storage allocation,
service activation,
resource release,
customer consumption,
billing events,
machine balancing,
and technical maintenance.
If every recognizable occurrence proceeds into full tax-event
formation, the governance system becomes dominated by
infrastructure activity rather than economically meaningful
operations.
ERSM screens these occurrences.
Internal load balancing and technical maintenance may be
recognized as genuine occurrences but screened as non-relevant to
further tax-event governance.
Customer resource consumption, service provision, billable
allocation, or economically meaningful transfers may remain
relevant and proceed.
ERSM therefore separates:
what genuinely happens inside a digital economy
from
what legitimately requires deeper tax-event governance. Use Case
2 — Autonomous Marketplace Where Machines Trade Resources
Continuously
An automated marketplace allows autonomous agents and machines to
purchase computing capacity, energy, data access, transport
capacity, and digital services from one another.
Millions of machine interactions occur every hour.
Some are quotations.
Some are rejected offers.
Some reserve capacity temporarily.
Some change resource availability.
Some create actual exchanges.
Some trigger settlements.
Some are merely technical negotiation messages.
A conventional system may either treat all interactions as
transactions or apply rigid rules that discard novel forms of
machine commerce.
ERSM evaluates recognized occurrences for plausible economic,
transactional, resource, and value-related relevance before
deeper Event Formation begins.
It does not determine whether a reservation, resource transfer,
machine exchange, or settlement is legally taxable.
Instead it ensures that potentially meaningful machine-economic
activity survives the recognition gateway while technical
negotiation noise does not automatically flood the downstream tax
architecture.
This establishes a crucial capability for future tax
infrastructure:
the system can scale with machine economies without equating
computational activity with taxable economic activity.
Canonical Boundary
ERSM governs whether a Recognized Occurrence possesses sufficientpotential relevance to remain within Tax-Relevant Event
Recognition. It does not determine the rules by which formal
recognition is made, the confidence of that recognition, whether
escalation is required, how the event is formed, or whether any
jurisdiction legally taxes it.
The TRERS internal progression remains:
ORIM — Did a distinguishable occurrence occur?
→ ERSM — Does the occurrence have sufficient potential relevance
to tax-event governance?
→ ERRM — What governed Recognition Rules determine progression?
→ ERCM — How strong is the recognition basis?
→ EREM — Does unresolved recognition require escalation?